feat(vla): align transformer training stack and rollout validation

tests/test_simple_robot_dataset_image_loading.py

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+import sys
+import tempfile
+import types
+import unittest
+from pathlib import Path
+from unittest import mock
+
+import h5py
+import numpy as np
+
+from roboimi.vla.data.simpe_robot_dataset import SimpleRobotDataset
+
+
+class SimpleRobotDatasetImageLoadingTest(unittest.TestCase):
+    def _write_episode(self, dataset_dir: Path) -> None:
+        episode_path = dataset_dir / "episode_0.hdf5"
+        with h5py.File(episode_path, "w") as root:
+            root.create_dataset("action", data=np.arange(8, dtype=np.float32).reshape(4, 2))
+            root.create_dataset(
+                "observations/qpos",
+                data=np.arange(16, dtype=np.float32).reshape(4, 4),
+            )
+            root.create_dataset("task", data=np.array([b"sim_transfer"]))
+            root.create_dataset(
+                "observations/images/front",
+                data=np.arange(4 * 8 * 8 * 3, dtype=np.uint8).reshape(4, 8, 8, 3),
+            )
+
+    def test_getitem_only_resizes_observation_horizon_images(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            dataset_dir = Path(tmpdir)
+            self._write_episode(dataset_dir)
+            dataset = SimpleRobotDataset(
+                dataset_dir,
+                obs_horizon=2,
+                pred_horizon=3,
+                camera_names=["front"],
+            )
+
+            resize_calls = []
+
+            def fake_resize(image, size, interpolation=None):
+                resize_calls.append(
+                    {
+                        "shape": tuple(image.shape),
+                        "size": size,
+                        "interpolation": interpolation,
+                    }
+                )
+                return image
+
+            fake_cv2 = types.SimpleNamespace(INTER_LINEAR=1, resize=fake_resize)
+
+            with mock.patch.dict(sys.modules, {"cv2": fake_cv2}):
+                sample = dataset[1]
+
+        self.assertEqual(len(resize_calls), 2)
+        self.assertEqual(tuple(sample["observation.front"].shape), (2, 3, 8, 8))